Due to the limited bit rate resources and quality dependency among frames, video coding performance is very sensitive to the QP configuration for the initial intra frame in RC. In video coding, each intra period is started with an intra coded frame, for which the initial QP determination is one of the key steps of RC initialization. The difficulty of obtaining a desirable initial QP comes from two reasons: 1) bit resources are always limited and bit allocation for intra frame actually means the trade-off on coding bit resources between intra frame and following inter frames; 2) the quality dependency between intra frame and following inter frames makes the bit allocation more complex. Therefore, the optimal initial QP determination can be deemed as a trade-off problem between intra and inter frames. Similarly, the precise problem modeling and accurate model parameters for optimal initial QP are difficult to achieve.
Regrettably, there is very little literature on refining RC initialization, particularly on using machine learning to find the optimal solution of initial QP. The existing typical non-learning based methods for initial QP determination are listed in a table in FIG. 1, which usually use two categories of influential factors: 1) target bit per pixel related, and 2) intra coding complexity related.
An early typical initial QP determination method is disclosed in JVT-0079 [1] for H.264/AVC, where three thresholds on target bit per pixel from the available bandwidth are used to set four fixed QPs. In TCSVT-2008 [2], the coding complexity is modeled with relation to entropy information and INTRA16 DC mode. In TBC-2009 [3] for H.264/AVC, the information of the edge vector amplitudes is considered to be related to the coding complexity. In TIE-2012 [4] for H.264/SVC, from intra frame and among specific frames, macroblock based variance (MBV) and sum of absolute difference (SAD) are extracted to generate the evaluation metric for coding complexity of intra coded content. It should be noticed that the above coding complexity related features may not be easily available in High Efficiency Video Coding (HEVC) and future video coding due to their high correlations with the particular coding tools in H.264/AVC and H.264/SVC. Therefore, it is better to make the coding complexity related feature independent of coding tools for easy adoption in future video coding.
Many of the model parameters in the existing calculation based initial QP determination methods are empirically achieved, and even differently configured for different videos with diverse resolutions. Therefore, the achieved model parameter cannot always be reliable for other different videos. These existing methods actually lack guarantees to obtain robustness for the coding performances achieved ultimately.
Another critical problem for the existing initial QP determination methods is the lack of effective optimization goals such that the efforts for optimization are useless for any of RC optimization goals, as well as for video coding performances. In general, the main RC optimization goals include improved R-D performance, lower quality fluctuations, higher bit rate achievement, and stable buffer occupancy control. Some existing initial QP methods evaluate coding distortion only to identify the best scheme, which is not accurate since different initial QPs will have different results on bit rate mismatch. The critical influence of initial QP on the overall video coding is embodied in the fact that different initial QPs will generate different coding results on both average bit rates and distortions. Although some conventional frame-level and block-level RC algorithms endeavor to make the final achieved bit rates be close to target bit rates, it will definitely fail for some unsatisfactory options of initial QPs. Therefore, it is obviously unacceptable to just compare the coding distortions to give the R-D performance evaluation and it is desirable to have an effective RC optimization scheme.
For HEVC, the latest reference software HM-16.14 [5] provides a unique R-λ model-based method for initial QP determination and parameter updating. Besides target bit per pixel of the current intra frame, the sum of the absolute transformed difference (SATD) is exploited as the coding complexity. The drawback of this initial QP determination method in HM-16.14 may easily come from the introduced inaccuracy from the empirical setting for the allocated bits of intra frame. This empirical bit setting makes the optimal initial QP achievement impossible, and then RC performance cannot be effectively optimized. Another problem is that the initial QP is predicted using the modulated R-λ model with inaccurate parameters. In fact, there is no guarantee that the intra frame bit allocation is optimal, as well as the accuracy of the final initial QP determination based on the modulated R-λ model. Therefore, it is preferable to have a better initial QP approach to avoid the inaccuracy of empirically setting intra frame bit allocation.